Abstract
Patient-specific modelling has emerged as a tool for studying heart function, demonstrating the potential to provide non-invasive estimates of tissue passive stiffness. However, reliable use of model-derived stiffness requires sufficient model accuracy and unique estimation of model parameters. In this paper we present personalised models of cardiac mechanics, focusing on improving model accuracy, while ensuring unique parametrisation. The influence of principal model uncertainties on accuracy and parameter identifiability was systematically assessed in a group of patients with dilated cardiomyopathy (n=3) and healthy volunteers (n=5). For all cases, we examined three circumferentially symmetric fibre distributions and two epicardial boundary conditions. Our results demonstrated the ability of data-derived boundary conditions to improve model accuracy and highlighted the influence of the assumed fibre distribution on both model fidelity and stiffness estimates. The model personalisation pipeline—based strictly on non-invasive data—produced unique parameter estimates and satisfactory model errors for all cases, supporting the selected model assumptions. The thorough analysis performed enabled the comparison of passive parameters between volunteers and dilated cardiomyopathy patients, illustrating elevated stiffness in diseased hearts.
Highlights
With cardiovascular disease being the leading cause of death worldwide,[37] significant research effort has been devoted to understanding heart function in health and pathology
The effect of the employed epicardial boundary condition on all models is summarised in Fig. 3, which presents the change in model error
Additional insight into cardiac function in DCM was obtained through patient-specific modelling, which enabled comparisons to transition from image-derived metrics to a model-based tissue stiffness
Summary
With cardiovascular disease being the leading cause of death worldwide,[37] significant research effort has been devoted to understanding heart function in health and pathology. As a wide range of aetiologies have been attributed to cardiac conditions, determining the factors influencing disease in individual patients—and selecting appropriate treatments—remains an ongoing challenge In some cases, such as hypertrophic cardiomyopathy, myocardial infarction and diastolic heart failure, abnormalities in tissue stiffness have been identified as features of the disease.[36]. A number of techniques have been proposed that merge clinical data with mathematical models of varying complexity to obtain an indirect approximation to patient-specific myocardial properties. These range from established chamber stiffness estimates derived from pressurevolume curves[5] or wall stress surrogates[1] to developing
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